THE SONG DECODERS 4

But while some of the genes involve expert, subjective judgment, they aren’t qualitative in the most traditional sense: there’s no rating that allows an analyst to conclude that a vocal or a sax solo is simply lousy. What Pandora’s system largely ignores is, in a word, taste. The way that Gasser or Westergren might put this is that it minimizes the influence of other people’s taste. Music-liking becomes a matter decided by the listener, and the intrinsic elements of what is heard. Early on, Westergren actually pushed for the idea that Pandora would not even reveal who the artist was until the listener asked. He thought maybe that structure would give users a kind of permission to evaluate music without even the most minimal cultural baggage. “We’re so insecure about our tastes,” he says.


While his partners talked him out of that approach, Westergren maintains “a personal aversion” to collaborative filtering or anything like it. “It’s still a popularity contest,” he complains, meaning that for any song to get recommended on a socially driven site, it has to be somewhat known already, by your friends or by other consumers. Westergren is similarly unimpressed by hipster blogs or other theoretically grass-roots influencers of musical taste, for their tendency to turn on artists who commit the crime of being too popular; in his view that’s just snobbery, based on social jockeying that has nothing to do with music. In various conversations, he defended Coldplay and Rob Thomas, among others, as victims of cool-taste prejudice. (When I ran Bob Lefsetz’s dismissal of Pandora by him, he laughed it off, and transitioned to arguing that Journey is, actually, a great band.)

He likes to tell a story about a Pandora user who wrote in to complain that he started a station based on the music of Sarah McLachlan, and the service served up a Celine Dion song. “I wrote back and said, ‘Was the music just wrong?’ Because we sometimes have data errors,” he recounts. “He said, ‘Well, no, it was the right sort of thing — but it was Celine Dion.’ I said, ‘Well, was it the set, did it not flow in the set?’ He said, ‘No, it kind of worked — but it’s Celine Dion.’ We had a couple more back-and-forths, and finally his last e-mail to me was: ‘Oh, my God, I like Celine Dion.’ ”

This anecdote almost always gets a laugh. “Pandora,” he pointed out, “doesn’t understand why that’s funny.”

By the time the Genome Project got under way, the idea of taking music apart and evaluating it by its acoustic elements was not actually new. “Machine listening” was pioneered in various university settings, often by people who had the exact same problem with collaborative filtering’s reliance on social data that Westergren has. Machine listening basically involves teaching computers to assess sound (or really, waveforms representing sound) into something resembling the way that humans hear it, with the goal of eliminating living, breathing listeners from the evaluation process completely.

Like collaborative filtering, machine listening can deal with a lot of data quickly. And when Westergren was trying to raise a second round of financing after the dot-com bust, most everyone involved in the business of music and technology had come to believe that any recommendation system needed to be able to handle millions of songs, instantly. A bunch of musicians sitting around discussing the finer points of drone and monophony wouldn’t cut it. “Everybody thought it was ridiculous,” Westergren agrees. He gave something like 350 pitches to venture capitalists over three years. “Most investors could not get over this idea that we were using humans.” But to Westergren, there were elements of music that machine listening just couldn’t capture — like the emotionality of a Getz solo. So yes, he wants listeners to experience new music on the basis of the music and not the influence of other people — but to do it right, people have to analyze the music.

Whatever the algorithmic equation, of course, there’s a listener on the other end who is much harder to decode. What you want to hear can depend on your mood, or whether you’re listening at work or in a nightclub. Context affects any cultural product, but music is different from, say, books or movies. Even a casual listener hears many thousands of songs; and to love a song is to take it in — whether attentively or as background music — over and over. Mick Jagger was once asked what makes a tune a classic, and the co-author of “(I Can’t Get No) Satisfaction” replied, “Repetition.” And yet, even the most conservative listener knows the feeling of hearing a hit single once too often. Maybe because music is so ubiquitous, we respond to it almost like food: sometimes we want to try the new restaurant, sometimes the comfort of a familiar favorite dish.

Still, are all these listener-specific factors really enough to explain what music we like, and why? “Music is an inherently social experience,” argues David Goodman, the president of CBS Interactive Music Group, which includes the popular Last.fm Internet radio service. Last.fm’s social-networking model revolves largely around this idea. “The way in which you experience music by sharing, by storytelling, being part of a community. Last.fm is built on what is organic to music.”

Ali Partovi, the C.E.O. of iLike, makes a related point. Used as an application on Facebook and similar sites, iLike bills itself as a “social music-discovery service” and claims more than 50 million registered users. There’s a huge difference, Partovi argues, between “this computer thinks you’ll like this song” and “your friend thinks you’ll like this song — even if it’s the same song.” The problem with a computer reading waveforms is that it “has no common sense,” summarizes Mike McCready, a founder of a company called Music Xray, a digital-music business for entertainment companies and artists. “It doesn’t take into consideration whether the artist is just starting out or they’re at the pinnacle of their career, it doesn’t take into consideration what they wore to the Grammys or who they’re dating or what they look like or what their age is. You have to factor all of this stuff in.”